Amy Daali, PhD

👉🏼 Understanding the Impact of Large Language Models in Healthcare Communication 🤓 Zefeng Yang 👇🏻 https://lnkd.in/eAtYVexV 🔍 Focus on data insights: - 📊 Large language models (LLMs) demonstrate advanced capabilities in natural language comprehension and generation, enhancing communication in healthcare settings. - 🧠 The evolution of LLMs parallels significant advancements in deep learning, particularly through transformer architecture, which improves their functionality. - 🔄 With extensive training data, LLMs can retain memory and comprehend complex medical information, facilitating better interactions between clinicians and patients. 💡 Main outcomes and implications: - 📝 LLMs can automate medical documentation, potentially reducing administrative burdens on healthcare providers. - 🤖 Future applications may include autonomous diagnosis and treatment suggestions, improving efficiency in patient care. - 📚 LLMs can assist in triage, summarizing medical documents, and personalizing patient education materials, thereby enhancing patient understanding and engagement. 📚 Field significance: - 🌐 The integration of LLMs in healthcare could revolutionize communication, leading to improved patient outcomes and satisfaction. - 🔍 Ongoing research is necessary to address limitations and ensure the safe and effective use of LLMs in real-world clinical settings. - 🚀 As technology advances, LLMs are poised to play a critical role in the future of healthcare delivery, particularly in specialized fields like ophthalmology. 🗄️: [#LargeLanguageModels #HealthcareCommunication #NaturalLanguageProcessing #DeepLearning #PatientCare #MedicalDocumentation #Ophthalmology #AIinHealthcare]

👉🏼 Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties 🤓 Felix Busch 👇🏻 https://lnkd.in/g6Gwkcf8 🔍 Focus on data insights: - GPT-4V demonstrated superior performance compared to GPT-4 in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection. - The integration of text and image analysis provided enhanced insights into radiology subspecialties. - Data-driven approaches utilizing GPT-4V showcased promising results for advanced radiological applications. 💡 Main outcomes and implications: - Improved accuracy and efficiency in radiological image analysis. - Enhanced diagnostic capabilities and decision-making support for radiologists. - Potential for increased automation and standardization in radiological practices. 📚 Field significance: - Advancements in artificial intelligence and natural language processing technologies are revolutionizing radiological applications. - Integration of text and image analysis tools can significantly impact the efficiency and accuracy of radiological diagnoses. - Data-driven insights from GPT-4V have the potential to reshape the future of radiology practice. 🗄️: #radiology #AI #textanalysis #imageanalysis #GPT-4V

👉🏼 Accuracy and Repeatability of ChatGPT Based on a Set of Multiple-Choice Questions on Objective Tests of Hearing 🤓 Krzysztof Kochanek 👇🏻 https://lnkd.in/exkWTN8g 🔍 Focus on data insights: - ChatGPT 4 showed higher accuracy (65-69%) compared to ChatGPT 3.5 (48-49%). - Short-term repeatability was assessed over four separate days, showing consistent performance improvements. - Percent agreement and Cohen's Kappa were used to evaluate response consistency over time. 💡 Main outcomes and implications: - ChatGPT 4 demonstrated superior accuracy and repeatability over ChatGPT 3.5. - Variability in responses raises concerns about professional applications of both versions. - The study highlights the importance of continuous improvement in AI models for reliable outcomes. 📚 Field significance: - Advances in AI technology like ChatGPT can enhance diagnostic processes in various fields. - Understanding the limitations and strengths of AI tools is crucial for informed decision-making in healthcare and other industries. 🗄️: [#accuracy #repeatability #AI #healthcare #diagnostics]

👉🏼 Testing the capacity of Bard and ChatGPT for writing essays on ethical dilemmas: A cross-sectional study 🤓 Mariano Kaliterna 👇🏻 https://lnkd.in/eRAA42PK 🔍 Focus on data insights: - 📊 AI-generated essays displayed a notable use of affective language, showcasing an ability to engage readers emotionally. - 🔍 Analytical thinking was more pronounced in AI essays, indicating a different cognitive processing style compared to human-written texts. - 🤖 Both ChatGPT and Bard produced essays that, while different in language characteristics, maintained coherence and relevance to personal experiences related to ethical challenges. 💡 Main outcomes and implications: - 🏫 The findings suggest a potential shift in how educational institutions may assess writing tasks, as AI can generate high-quality text akin to student submissions. - 👩‍🎓 Educators must adapt their pedagogical approaches to account for the capabilities of LLMs, possibly reevaluating the importance of traditional essay assignments. - ⚖️ Ethical discussions surrounding the use of AI in education need to be prioritized, focusing on issues of authenticity and academic integrity. 📚 Field significance: - 🌐 The study contributes to the ongoing discourse on AI's role in education, shedding light on both its capabilities and the implications for student learning. - 📈 It emphasizes the necessity for educational frameworks to evolve in response to technological advancements, ensuring integrity and effectiveness in assessment methods. 🗄️: [#artificialintelligence #education #largelanguagemodels #ethicsineducation #academicwriting #AIimpact #pedagogy]

👉🏼 Getting to grips with generative AI 🤓 Sam Foster 👇🏻 https://lnkd.in/egui4vrZ 🔍 Focus on data insights: - The use of generative AI tools like ChatGPT is raising important considerations for regulators and assessors. - Data insights from these tools are shaping the way regulatory bodies approach their roles. - Understanding the implications of data generated by AI systems is crucial for effective decision-making in regulatory practices. 💡 Main outcomes and implications: - Regulators and assessors need to adapt to the increasing presence of generative AI tools in their work. - Proper utilization of data insights from AI can enhance regulatory processes and decision-making. - Addressing ethical and legal implications of AI-generated data is essential for maintaining regulatory standards. 📚 Field significance: - The integration of generative AI tools in regulatory practices is reshaping the landscape of oversight and assessment. - Embracing data-driven insights from AI technologies can lead to more efficient and informed regulatory decisions. - Continuous evaluation and adaptation to AI advancements are key for staying relevant in regulatory frameworks. 🗄️: [#generativeAI #datainsights #regulatorypractices #AIethics]

🤖 A new chatbot, gpt2-chatbot, has been causing quite a stir online. Rumors are swirling that it could be a new version of GPT-2, trained using new techniques, or even a test version of GPT-4.5 or GPT-5 in disguise. https://lnkd.in/dd_iVbTf #artificialinteligence #llm #chatbot #machinelearning

👉🏼 A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions 🤓 Scott P McGrath 👇🏻 https://lnkd.in/eBHetz2N 🔍 Focus on data insights: - Improved accuracy in GPT-4 over GPT-3.5 (P < .0001) - Notable errors in accuracy remained - Relevance of responses varied in GPT-4, generally favorable 💡 Main outcomes and implications: - GPT-4 shows potential in genetic education with notable progress - Challenges include outdated information and the need for ongoing refinement - Emphasizes balancing technological innovation with ethical responsibility in healthcare information delivery 📚 Field significance: - Genetic information delivery - Technology in medical settings - Ethical implications in healthcare 🗄️: [#genetics #ChatGPT #datainsights #medicaltechnology]

👉🏼 Evaluating the Performance of ChatGPT in Answering Questions on Carbon Monoxide Poisoning 🤓 Jun Qiu 👇🏻 https://lnkd.in/ebbZPijy 🔍 Focus on data insights: - 📊 Approximately 50% of ChatGPT's responses showed high consistency, indicating moderate reliability in its outputs. - ❓ One-third of the responses displayed significant inaccuracies or vague answers, signaling areas in need of improvement. - 🌐 The accuracy rates were 61.1% in Chinese and 57% in English, highlighting a disparity between language performance. 💡 Main outcomes and implications: - 🔧 The findings suggest that while ChatGPT can provide useful insights, its current version requires enhancements in both consistency and accuracy. - 🩺 This study emphasizes the necessity for continued human oversight in clinical settings, as AI-generated responses may not always be dependable for critical healthcare decisions. - 🚀 Future developments should aim to improve data handling capabilities and accuracy metrics for AI tools used in the medical field. 📚 Field significance: - 🧠 Understanding AI's role in medicine is crucial as it becomes more integrated into healthcare, necessitating studies like this to gauge effectiveness. - 🔍 The research contributes to ongoing conversations about the potential and limitations of AI technology in real-world applications, particularly in sensitive areas like healthcare. 🗄️: [#AI #Healthcare #ChatGPT #CarbonMonoxidePoisoning #MedicalReliability #DataInsights #AIInMedicine #ClinicalDecisions]

👉🏼 Evaluating the strengths and weaknesses of large language models in answering neurophysiology questions 🤓 Hassan Shojaee-Mend 👇🏻 https://lnkd.in/eZEbA_zF 🔍 Focus on data insights: - Large language models (LLMs) showcase remarkable natural language processing capabilities. - Evaluation of LLMs' proficiency in neurophysiology is crucial for research, education, and clinical applications. - Assessment of LLMs in answering neurophysiology questions in English and Persian (Farsi) reveals insights into their utility. 💡 Main outcomes and implications: - LLMs demonstrate good performance in answering neurophysiology questions. - No significant difference observed between English and Persian languages or cognitive levels. - Models excel in motor system topics but show weaknesses in integrative topics. 📚 Field significance: - Understanding LLMs' capabilities and limitations in neurophysiology research. - Highlighting the need for targeted training to address knowledge gaps and enhance reasoning skills. - Importance of domain-specific assessments for evaluating advancements in LLMs' performance. 🗄️: [#naturallanguageprocessing #neurophysiology #LLMs #research #datainsights]

👉🏼 Perils and opportunities in using large language models in psychological research 🤓 Suhaib Abdurahman 👇🏻 https://lnkd.in/e9an387i 🔍 Focus on data insights: - Highlighted the importance of recognizing global psychological diversity - Cautioned against treating LLMs as universal solutions for text analysis - Advocated for developing transparent, open methods to address LLMs' opaque nature 💡 Main outcomes and implications: - Showed concrete impact in various empirical studies - Argued for diversifying human samples and expanding psychology's methodological toolbox - Promoted an inclusive, generalizable science and countered over-reliance on LLMs 📚 Field significance: - Psychological research - Large language models - Text analysis 🗄️: [#psychologicalresearch #largelanguagemodels #textanalysis]